Following [1]}, we denote a 4D LF as \(L(u,v,x,y)\) , where \((u,v)\)  represents the angular dimension and \((x,y)\)  represents the spatial dimension. Following most existing SR methods [2]}, [3]}, we only super-resolve the Y channel images which is converted from the RGB color space to YCbCr color space. As illustrated in Fig. REF (a), our network consists of an initial feature extraction module, a dense dual-attention module, a fusion module, and an upscale module. In this section, we describe the proposed network in detail.
